Prediction of Future Groundwater Contamination Risk in Rural Agricultural Regions
Bibliographic record
Abstract
Strategies for understanding regional groundwater contamination risk are often challenged by changing land use and climate conditions. Furthermore, index-based assessment methods are typically implemented in a static manner which inherently precludes possible changes in future contamination risk resulting from these dynamic conditions. It is perhaps equally important to consider the manner in which climate forcing and land use are represented. With regards to land use in particular, rural regions may have unique concerns; agricultural land use is commonly represented as a single land use class despite the complex land management practices that may be present and the subsequent implications for groundwater quality. This investigation demonstrates alteration of the conventional DRASTIC-LU methodology to assess mid-century changes in groundwater contamination risk through the treatment of recharge, depth to water table, and land use as dynamic factors. The potential influence of agricultural land use representation on DRASTIC-LU model performance and prediction is concurrently examined. The Upper Parkhill watershed in southwestern Ontario, Canada is explored as a case study for method application. Study results indicate that the inclusion of crop rotation and tile drainage data has the potential to improve model functioning. Moreover, predicted future changes in groundwater contamination risk may differ depending on the manner in which agricultural land use is represented. This investigation helps to resolve the influence of land use on groundwater contamination risk and provides a screening tool that may be used to support groundwater decision making.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".